ST202 is also recommended. Statistical decision theory: risk, decision rules, loss and utility functions, Bayesian expected loss, Frequentist risk. Bayesian Inference: Bayes theorem, prior, posterior ...

id=7745) Statistical decision theory: risk, decision rules, loss and utility functions, Bayesian expected loss, Frequentist risk. Bayesian Inference: Bayes theorem, prior, posterior and predictive ...

The suggested augmentation robustifies the baseline parametric model to local misspecification, while preserving the appeal of Bayesian inference. We develop an MCMC algorithm for the augmented model ...

The Multi-source Probabilistic Inference (MUPI) research group studies statistical machine learning and artificial intelligence. We develop new methods and algorithms for coping with uncertainty in ...

empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates ...

We looked at who signed up for Montgomery County’s “listening sessions” on a housing proposal and one-third of them came from ...

Integrated Inferences develops a framework for using causal models and Bayesian updating for qualitative and mixed-methods research. By making, updating, and querying causal models, researchers are ...